Properties of graph distance measures by means of discrete inequalities

Matthias Dehmer; Zengqiang Chen; Frank Emmert-Streib; Yongtang Shi; Shailesh Tripathi; Aliyu Musa; Abbe Mowshowitz

doi:10.1016/j.apm.2018.01.027

Properties of graph distance measures by means of discrete inequalities

Matthias Dehmer^*
, Zengqiang Chen
, Frank Emmert-Streib
, Yongtang Shi
, Shailesh Tripathi
, Aliyu Musa
, Abbe Mowshowitz

^*Corresponding author for this work

Upper Austria University of Applied Sciences
Nankai University
Private University for Health Sciences, Medical Informatics and Technology
Tampere University
City University of New York

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

In this paper, we investigate graph distance measures based on topological graph measures. Those measures can be used to measure the structural distance between graphs. When studying the scientific literature, one is aware that measuring distance/similarity between graphs meaningfully has been intricate. We demonstrate that our measures are well-defined and prove bounds for investigating their value domain. Also, we generate numerical results and demonstrate that the measures have useful properties.

Original language	English
Pages (from-to)	739-749
Number of pages	11
Journal	Applied Mathematical Modelling
Volume	59
DOIs	https://doi.org/10.1016/j.apm.2018.01.027
Publication status	Published - Jul 2018
Externally published	Yes

Keywords

Distance measures
Graphs
Inequalities
Networks
Similarity measures

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10.1016/j.apm.2018.01.027

Cite this

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abstract = "In this paper, we investigate graph distance measures based on topological graph measures. Those measures can be used to measure the structural distance between graphs. When studying the scientific literature, one is aware that measuring distance/similarity between graphs meaningfully has been intricate. We demonstrate that our measures are well-defined and prove bounds for investigating their value domain. Also, we generate numerical results and demonstrate that the measures have useful properties.",

keywords = "Distance measures, Graphs, Inequalities, Networks, Similarity measures",

author = "Matthias Dehmer and Zengqiang Chen and Frank Emmert-Streib and Yongtang Shi and Shailesh Tripathi and Aliyu Musa and Abbe Mowshowitz",

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AU - Dehmer, Matthias

AU - Chen, Zengqiang

AU - Emmert-Streib, Frank

AU - Shi, Yongtang

AU - Tripathi, Shailesh

AU - Musa, Aliyu

AU - Mowshowitz, Abbe

PY - 2018/7

Y1 - 2018/7

N2 - In this paper, we investigate graph distance measures based on topological graph measures. Those measures can be used to measure the structural distance between graphs. When studying the scientific literature, one is aware that measuring distance/similarity between graphs meaningfully has been intricate. We demonstrate that our measures are well-defined and prove bounds for investigating their value domain. Also, we generate numerical results and demonstrate that the measures have useful properties.

AB - In this paper, we investigate graph distance measures based on topological graph measures. Those measures can be used to measure the structural distance between graphs. When studying the scientific literature, one is aware that measuring distance/similarity between graphs meaningfully has been intricate. We demonstrate that our measures are well-defined and prove bounds for investigating their value domain. Also, we generate numerical results and demonstrate that the measures have useful properties.

KW - Distance measures

KW - Graphs

KW - Inequalities

KW - Networks

KW - Similarity measures

UR - https://www.scopus.com/pages/publications/85044481326

U2 - 10.1016/j.apm.2018.01.027

DO - 10.1016/j.apm.2018.01.027

M3 - Article

AN - SCOPUS:85044481326

SN - 0307-904X

VL - 59

SP - 739

EP - 749

JO - Applied Mathematical Modelling

JF - Applied Mathematical Modelling

ER -

Properties of graph distance measures by means of discrete inequalities

Abstract

Keywords

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